Inspiration

Problem With COVID-19 pandemic maturing well beyond the mere infancy stages of what was anticipated, it is of utmost importance to follow guidelines set forth by health organizations in this time of uncertainty and misinformation. Words simply cannot put into perspective the ramifications of this outbreak. There are currently over three million cases internationally and over two hundred thousand deaths attributed to the virus.

While the officials in healthcare organizations are indeed doing everything in their power to ensure the welfare of people, the virus continues to propagate through communities, yielding unprecedented effects. Grocery stores and pharmacies are running low on previously accessible and essential products, such as toilet paper and hand sanitizer, and therefore cannot meet the demands of distressed shoppers. Citizens are simply unaware of how to obtain necessary resources while limiting the spread of this deadly virus.

Currently, all guidelines revolve around the singular idea of social distancing and there has been minimal research with the objective of pinpointing other factors, such as those pertaining to environmental conditions. Although social distancing is undoubtedly among some of the most effective preventative measures being taken to slow the spread, it is not entirely a reasonable expectation. People still need to leave their homes to shop for essential items, get in some physical exercise and complete other tasks, and it is amidst these errands that one is most susceptible to the virus . Unfortunately, there are no concrete specifications for this. While online shopping is indeed a comfortable alternative, services provided by Amazon and Instacart among others are unable to fulfill orders at this time because of the high demand.

Motivation Seeing the infeasibility of enforcing social distancing at all times, we have decided to analyze epidemic data and patient data to find other factors that may influence the spread of disease. Through the use of geographical information systems, simulations of the spread, and analysis of environmental conditions, we have been able to examine the intricate interplay of different factors and ultimately identify some of the most influential parameters that could effectively be optimized to reduce spread. The two factors that we have identified and decided to form as the basis for our solution are climate and the rate of reproduction metric.

What it does

Solution The proposed solution to the stated problem is a web application that caters to the needs of users through two main features: scheduling tasks (powered by the weather engine) and planning shopping trips (powered by location and weather engine as well as crowdsourcing).

Click here More Information *scroll to weather engine & location engine section for in depth description of our technology.

What's next for Optime

While our current implementation works, we feel that we can integrate more features that could potentially improve the accuracy of our data and quality of our service.

Predictive Analysis - A Deep Learning Approach Seeing the potential of deep learning models in other applications pertaining to weather analysis, it seems reasonable to utilize an artificial neural network trained on climate data as the features and case data as the labels. There exists an abundance of data related to these attributes on the web publicized for developers to use. In our next iteration, we intend on training a predictive model, which could be more accurate than our current weather & location engine.

Store APIs + Shopkeeper Update Form One major flaw in crowdsourcing is validating the credibility of the updated information. We are taking appropriate measures to ensure quality of the user inputs, but there is certainly room for improvement. Our idea to mitigate this problem was to introduce a new page for shopkeepers to update their inventory directly, making it easier for users to access the latest & most accurate data.

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